An Ensemble Classifier of Support Vector Machines Used to Predict Protein Structural Classes by Fusing Auto Covariance and Pseudo-Amino Acid Composition

2010 ◽  
Vol 29 (1) ◽  
pp. 62-67 ◽  
Author(s):  
Jiang Wu ◽  
Meng-Long Li ◽  
Le-Zheng Yu ◽  
Chao Wang
Keyword(s):  
Amino Acid ◽  
Support Vector Machines ◽  
Amino Acid Composition ◽  
Acid Composition ◽  
Ensemble Classifier ◽  
Support Vector ◽  
Pseudo Amino Acid Composition ◽  
Vector Machines ◽  
Auto Covariance ◽  
Structural Classes

scholarly journals Identifying the Types of Ion Channel-Targeted Conotoxins by Incorporating New Properties of Residues into Pseudo Amino Acid Composition

2016 ◽  
Vol 2016 ◽  
pp. 1-5 ◽  
Author(s):  
Yun Wu ◽  
Yufei Zheng ◽  
Hua Tang
Keyword(s):  
Amino Acid ◽  
Ion Channel ◽  
Amino Acid Composition ◽  
Acid Composition ◽  
Current Method ◽  
Computational Method ◽  
Support Vector ◽  
Pseudo Amino Acid Composition ◽  
Feature Selection Technique ◽  
Drug Candidates

Conotoxins are a kind of neurotoxin which can specifically interact with potassium, sodium type, and calcium channels. They have become potential drug candidates to treat diseases such as chronic pain, epilepsy, and cardiovascular diseases. Thus, correctly identifying the types of ion channel-targeted conotoxins will provide important clue to understand their function and find potential drugs. Based on this consideration, we developed a new computational method to rapidly and accurately predict the types of ion-targeted conotoxins. Three kinds of new properties of residues were proposed to use in pseudo amino acid composition to formulate conotoxins samples. The support vector machine was utilized as classifier. A feature selection technique based onF-score was used to optimize features. Jackknife cross-validated results showed that the overall accuracy of 94.6% was achieved, which is higher than other published results, demonstrating that the proposed method is superior to published methods. Hence the current method may play a complementary role to other existing methods for recognizing the types of ion-target conotoxins.

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